Parsing error correction of medical phrases for semantic annotation of clinical radiology reports.

The purpose of this study is to develop a module for correcting errors in the product of a natural language parser. When tested with 300 CT reports, a total of 604 patterns were generated. The recall and precision was improved to 90.7% and 74.1% after processed by the module from initial 80.5% and 42.8% respectively. This rule-based module will help health care personnel reduce the cost of manual tagging correction for corpus building.